In this paper, the problem of multi-UAVs (Unmanned Arial Vehicles) Visual Simultaneous Localization and Mapping (SLAM) is considered by using a new framework for pose and map estimation using monocular vision and reduced communications capabilities. The problem of localization and mapping is solved by fusing monocular visual data with odometry measurements through Graph SLAM formulation. Using each robot’s map data representation, a proposal is made for a good and robust communication between UAVs to perform efficient data exchange while keeping SLAM performances. A mesh network is chosen to import solutions to wireless networking. Finally, some validation experiments are performed in an Ad Hoc Network and a Wireless Mesh Network using Better Approach to Mobile Ad Hoc Network (BATMAN) protocol.